# Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation

arXiv: https://arxiv.org/abs/2402.10887 · HTML: https://mlsanity.com/paper/2402.10887/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 30 of its 42 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation](https://mlsanity.com/paper/2401.13560/) (2024)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation](https://mlsanity.com/paper/2402.05079/) (2024)
- [Normalized Cut Loss for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1804.01346/) (2018)
- [Selective Structured State-Spaces for Long-Form Video Understanding](https://mlsanity.com/paper/2303.14526/) (2023)
- [Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer](https://mlsanity.com/paper/2112.04894/) (2021)
- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision](https://mlsanity.com/paper/2203.02106/) (2022)
- [AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation](https://mlsanity.com/paper/2110.10403/) (2021)
- [Gated CRF Loss for Weakly Supervised Semantic Image Segmentation](https://mlsanity.com/paper/1906.04651/) (2019)
- [Mumford-Shah Loss Functional for Image Segmentation with Deep Learning](https://mlsanity.com/paper/1904.02872/) (2019)
- [ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation](https://mlsanity.com/paper/2402.02029/) (2024)
- [RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels](https://mlsanity.com/paper/2009.12873/) (2020)
- [A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images](https://mlsanity.com/paper/2002.04493/) (2020)
- [Triple-View Feature Learning for Medical Image Segmentation](https://mlsanity.com/paper/2208.06303/) (2022)

## Built on by (most cited first, 29 of 29)

- [Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation](https://mlsanity.com/paper/2404.04256/) (2024)
- [Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges](https://mlsanity.com/paper/2404.16112/) (2024)
- [MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection](https://mlsanity.com/paper/2403.02148/) (2024)
- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [A Survey on Visual Mamba](https://mlsanity.com/paper/2404.15956/) (2024)
- [Visual Mamba: A Survey and New Outlooks](https://mlsanity.com/paper/2404.18861/) (2024)
- [Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study](https://mlsanity.com/paper/2405.08493/) (2024)
- [MVGamba: Unify 3D Content Generation as State Space Sequence Modeling](https://mlsanity.com/paper/2406.06367/) (2024)
- [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024)
- [A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond](https://mlsanity.com/paper/2410.02362/) (2024)
- [UU-Mamba: Uncertainty-aware U-Mamba for Cardiac Image Segmentation](https://mlsanity.com/paper/2405.17496/) (2024)
- [A Mamba-based Siamese Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2407.06839/) (2024)
- [Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models](https://mlsanity.com/paper/2409.18654/) (2024)
- [VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module](https://mlsanity.com/paper/2404.05105/) (2024)
- [AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation](https://mlsanity.com/paper/2405.03011/) (2024)
- [MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs](https://mlsanity.com/paper/2404.13884/) (2024)
- [SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds](https://mlsanity.com/paper/2505.10601/) (2025)
- [AI-driven View Guidance System in Intra-cardiac Echocardiography Imaging](https://mlsanity.com/paper/2409.16898/) (2024)
- [Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images](https://mlsanity.com/paper/2503.17261/) (2025)
- [SegResMamba: An Efficient Architecture for 3D Medical Image Segmentation](https://mlsanity.com/paper/2503.07766/) (2025)
- [Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models](https://mlsanity.com/paper/2503.11224/) (2025)
- [Venturing into Uncharted Waters: The Navigation Compass from Transformer to Mamba](https://mlsanity.com/paper/2406.16722/) (2024)
- [TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery](https://mlsanity.com/paper/2407.05736/) (2024)
- [MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation](https://mlsanity.com/paper/2501.07120/) (2025)
- [Diff-CL: A Novel Cross Pseudo-Supervision Method for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.09408/) (2025)
- [TAPM-Net: Trajectory-Aware Perturbation Modeling for Infrared Small Target Detection](https://mlsanity.com/paper/2601.05446/) (2026)
- [CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation](https://mlsanity.com/paper/2603.19659/) (2026)
- [Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention](https://mlsanity.com/paper/2605.10581/) (2026)

## Abstract (arXiv)

Medical image segmentation is increasingly reliant on deep learning techniques, yet the promising performance often come with high annotation costs. This paper introduces Weak-Mamba-UNet, an innovative weakly-supervised learning (WSL) framework that leverages the capabilities of Convolutional Neural Network (CNN), Vision Transformer (ViT), and the cutting-edge Visual Mamba (VMamba) architecture for medical image segmentation, especially when dealing with scribble-based annotations. The proposed WSL strategy incorporates three distinct architecture but same symmetrical encoder-decoder networks: a CNN-based UNet for detailed local feature extraction, a Swin Transformer-based SwinUNet for comprehensive global context understanding, and a VMamba-based Mamba-UNet for efficient long-range dependency modeling. The key concept of this framework is a collaborative and cross-supervisory mechanism that employs pseudo labels to facilitate iterative learning and refinement across the networks. The effectiveness of Weak-Mamba-UNet is validated on a publicly available MRI cardiac segmentation dataset with processed scribble annotations, where it surpasses the performance of a similar WSL framework utilizing only UNet or SwinUNet. This highlights its potential in scenarios with sparse or imprecise annotations. The source code is made publicly accessible.